The agent-based model is the principal scientific instrument of generative social science, whose principal tenet is that: to explain a macroscopic pattern, it does not suffice to demonstrate that the pattern is an equilibrium. Rather one must demonstrate, mathematically or computationally, how the pattern could emerge on time scales of interest in a population of cognitively plausible agents. Although systematically violated by humans, the rational actor model persists as the dominant idealization in the social sciences for lack of explicit formal alternatives. Epstein’s Agent_Zero is one candidate. Based on cognitive neuroscience, Agent Zero generates important macro-phenomena and group behaviors that violate Rational Choice Theory. Applications to violence, epidemics, and financial contagions are discussed. Agent_Zero is an example of the “intelligent design” of completed agents—fully endowed with rules and parameters—to grow macroscopic target patterns from the bottom up. Inverse generative science (iGSS) stands this approach on its head: Rather than handcrafting completed agents to grow a target—the forward problem—we start with the macro-target and evolve micro-agents that generate it, stipulating only primitive agent-rule constituents and permissible combinators. Rather than specific agents as designed inputs, we are interested in agents—indeed, families of agents—as evolved outputs. This is the backward problem and tools from AI and Evolutionary Computing can solve it, extending the reach of agent-based modeling, while addressing common criticisms of it. Examples of iGSS and outstanding foundational issues surrounding it are discussed. An important goal of iGSS is to evolve further formal alternatives to the Rational Actor, with Agent_Zero as one possible point of evolutionary departure.